Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

In this paper we demonstrate a novel alternating direction method of multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy management problem considering both power split and engine on/off decisions. The solution of a convex relaxation of the problem is used to initialize the optimization, which is necessarily nonconvex, and whilst only local convergence can be guaranteed, it is demonstrated that the algorithm will terminate with the optimal power split for the given engine switching sequence. The algorithm is compared in simulation against a charge-depleting/charge-sustaining (CDCS) strategy and dynamic programming (DP) using real world driver behaviour data, and it is demonstrated that the algorithm achieves 90\% of the fuel savings obtained using DP with a 3000-fold reduction in computational time

Parallel ADMM for robust quadratic optimal resource allocation problems

An alternating direction method of multipliers (ADMM) solver is described for optimal resource allocation problems with separable convex quadratic costs and constraints and linear coupling constraints. We describe a parallel implementation of the solver on a graphics processing unit (GPU) using a bespoke quartic function minimizer. An application to robust optimal energy management in hybrid electric vehicles is described, and the results of numerical simulations comparing the computation times of the parallel GPU implementation with those of an equivalent serial implementation are presented

How scaling of the disturbance set affects robust positively invariant sets for linear systems

This paper presents new results on robust positively invariant (RPI) sets for linear discrete-time systems with additive disturbances. In particular, we study how RPI sets change with scaling of the disturbance set. More precisely, we show that many properties of RPI sets crucially depend on a unique scaling factor which determines the transition from nonempty to empty RPI sets. We characterize this critical scaling factor, present an efficient algorithm for its computation, and analyze it for a number of examples from the literature

Exploring Infant Leukemia through Exome Sequencing and an In Vitro Model of Hematopoietic Development

Cancer is a heterogeneous disease with myriad causes and outcomes. Many of the cancers that occur in adult populations have become increasingly well characterized with the advent of affordable high-throughput sequencing. These studies have revealed that cancer is largely a disease of somatic mutation in the adult population. In strong contrast to this, childhood cancers have an exceedingly low rate of somatic mutation. At the extreme end of this spectrum is Infant Leukemia (IL). Sequencing of IL has revealed that these tumors frequently have one or fewer somatic SNP. In the absence of a somatic explanation for IL, many other possible explanations have been put forth. To date, however, none of these has been able to fully explain the incidence of this disease. In this context, we hypothesized that inherited germline variation, rather than somatically acquired mutations, played a role in the development of IL. We showed that IL patients have an excess of rare, non-synonymous inherited variation in known-leukemia associated genes. We further showed that there are several genes that harbor far more putatively damaging variation in IL patients than either control exomes or population databases. These highly variant genes are intolerant of loss-of-function changes, and most fall into one of three critical cellular functions. Together, these data suggest that IL is indeed a result of predisposing genetic variation. Obtaining a clear functional understanding of IL has been hindered by the lack of an appropriate model. The fact that this disease arises in utero makes it difficult to study in vivo, and no animal models have been able to recapitulate the rapid onset of disease. In recent years, several groups have developed in vitro models of hematopoiesis. While these are not yet able to fully capture all aspects of hematopoietic development, they do provide a system in which we can explore the effects of the genetic variation observed in IL patients in a controlled and developmentally relevant setting. Importantly, we are able to effectively separate the primitive and definitive hematopoietic programs and explore each independently, a necessary feature for any IL model. In this work, we present the first steps in the development of a model of IL that is consistent with our sequencing findings. While we do not achieve leukemic transformation, we do show that cells deficient in MLL3, a gene that was frequently variant in our IL cohorts, have a marked impairment in both primitive and definitive hematopoiesis. We find that this is evident both based on surface markers and colony forming ability. In addition to these functional characteristics, we show that the transcriptional and epigenetic profiles of the MLL3-knockout cells are greatly perturbed, consistent with the role of MLL3 as a transcriptional enhancer and epigenetic regulator. These results provide insight into the etiology of IL as a disease of aberrant development, and provide a basis for the establishment of an in vitro model of IL

Model Predictive Control approach for guidance of spacecraft rendezvous and proximity maneuvering

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92370/1/rnc2827.pd

Robust receding horizon control for convex dynamics and bounded disturbances

A novel robust nonlinear model predictive control strategy is proposed for systems with convex dynamics and convex constraints. Using a sequential convex approximation approach, the scheme constructs tubes that contain predicted trajectories, accounting for approximation errors and disturbances, and guaranteeing constraint satisfaction. An optimal control problem is solved as a sequence of convex programs, without the need of pre-computed error bounds. We develop the scheme initially in the absence of external disturbances and show that the proposed nominal approach is non-conservative, with the solutions of successive convex programs converging to a locally optimal solution for the original optimal control problem. We extend the approach to the case of additive disturbances using a novel strategy for selecting linearization points and seed trajectories. As a result we formulate a robust receding horizon strategy with guarantees of recursive feasibility and stability of the closed-loop system

An ADMM Algorithm for MPC-based Energy Management in Hybrid Electric Vehicles with Nonlinear Losses

In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for energy management in hybrid electric vehicles, and an Alternating Direction Method of Multipliers (ADMM) algorithm for its solution. We develop a new proof of convexity for the problem that allows the nonlinear dynamics to be modelled as a linear system, then demonstrate the performance of ADMM in comparison with Dynamic Programming (DP) through simulation. The results demonstrate up to two orders of magnitude improvement in solution time for comparable accuracy against DP